1. Field of the Invention
The invention relates to cardiology. More specifically, the invention relates to non-invasive identification and management of individuals at risk for sudden cardiac death. Cardiac vulnerability to ventricular fibrillation, the mode of sudden death, is dynamically tracked by analysis of an electrocardiogram.
2. Related Art
Sudden cardiac death (SCD), which claims over 350,000 lives annually in the United States, results from abrupt disruption of heart rhythm primarily due to ventricular fibrillation. Fibrillation occurs when transient neural triggers impinge upon an electrically unstable heart causing normally organized electrical activity to become disorganized and chaotic. Complete cardiac dysfunction results.
The first step in preventing sudden cardiac death is identifying those individuals whose hearts are electrically unstable. This is a major objective in cardiology. If vulnerable individuals can be reliably identified non-invasively, then prevention will be aided, mass screening will become possible, and pharmacologic management of vulnerable individuals can be tailored to prevent ventricular fibrillation.
Programmed cardiac electrical stimulation has been used in patients to provide quantitative information on susceptibility and on the effectiveness of their pharmacologic therapy. Unfortunately, this method requires cardiac catheterization and introduces the hazard of inadvertent induction of ventricular fibrillation. Therefore, it is used only in severely ill patients and is performed only in hospitals. It is unsuitable for mass screening.
A technique which has shown great promise is that of analyzing alternans in the T-wave of an electrocardiogram (ECG). As used throughout this disclosure, the term "T-wave" is defined to mean the portion of an ECG which includes both the T-wave and the ST segment. Alternans in the T-wave results from different rates of repolarization of the muscle cells of the ventricles. The extent to which these cells recover (or repolarize) non-uniformly is the basis for electrical instability of the heart.
The consistent occurrence of alternans in the T-wave prior to fibrillation is well established. Thus, detection of alternans promises to be a useful tool in predicting vulnerability to fibrillation, if an accurate method of quantifying the alternans can be developed. The following are examples of conventional attempts to quantify alternation in an ECG signal: Dan R. Adam et al., "Fluctuations in T-Wave Morphology and Susceptibility to Ventricular Fibrillation," Journal of Electrocardiology, vol. 17 (3), 209-218 (1984); Joseph M. Smith et al. "Electrical alternans and cardiac electrical instability," Circulation, vol. 77, No. 1, 110-121 (1988); U.S. Pat. No. 4,732,157 to Kaplan et al.; and U.S. Pat. No. 4,802,491 to Cohen et al.
Smith et al. and Cohen et al. disclose methods for assessing myocardial electrical instability by power spectrum analysis of the T-wave. These methods derive an alternating ECG morphology index from a series of heartbeats. Sample point matrices are constructed and the alternating energy at each of the sample points is computed using the analytical method of multi-dimensional power spectral estimation which is calculated by constructing the discrete Fourier transform of the Hanning-windowed sample auto-correlation function. The alternating energy over the entire set of sample points is summed to generate the total alternating energy and then normalized with respect to the average waveform to produce an "alternating ECG morphology index (AEMI)."
While a powerful tool, Fourier power spectrum analysis averages time functions over the entire time series so that rapid arrhythmogenic changes, such as those due to neural discharge and reperfusion, are not detected because data from these events are intrinsically non-stationary.
Kaplan et al. disclose a method for quantifying cycle-to-cycle variation of a physiologic waveform such as the ECG for the purpose of assessing myocardial electrical stability. A physiologic waveform is digitized and sampled and a scatter plot of the samples is created. Non-linear transformation of the sample points determines a single parameter which attempts to quantify the degree of alternation in the sampled waveform and which is associated with the susceptibility of the physiologic waveform to enter into an aperiodic or chaotic state. Kaplan et al. suggest that "measurement of this parameter! may provide an index of ECG waveform variability which may provide an improved correlation with susceptibility to ventricular fibrillation than previously available indices. " See col.3, lines 15-19. Whether ventricular fibrillation is a chaotic state, however, is still very much in debate. See D. T. Kaplan and R. J. Cohen, "Searching for chaos in fibrillation," Ann. N.Y. Acad. Sci., vol. 591, pp. 367-374, 1990.
Adam et al. disclose a non-invasive method which involves spectral analysis of the alternation from beat-to-beat morphology of the ECG complex. The alternation of T-wave energy from beat-to-beat was measured to generate a T-wave alternation index (TWAI). This technique is unable to detect alternation in waveform morphology which results in alternating wave shapes of equal energy. In addition, the amount of alternation detected per this method is dependent on the static portion of the wave shape. That is, the same amount of alternation superimposed on a different amplitude signal will result in different values for the T-wave alternation index such that this technique could completely obscure the presence of alternation in the original waveform morphologies.
In the absence of an effective method for dynamically quantifying the magnitude of alternation, identification of alternans as a precursor of life-threatening arrhythmias and provision of a test for cardiac vulnerability have been unattainable. In addition, the conventional attempts to quantify alternans have employed inferior methods of alternans (i.e., ECG) sensing. The ECG signals used for the Cohen et al. analysis were sensed via epicardial (i.e., heart surface) electrodes or via lateral limb, rostral-caudal, and dorsal-ventral leads. Smith et al. sensed via leads I, aVF, and V.sub.1-2, Adam et al. utilized ECG lead I "because in this lead the ratio of the amplitude of the pacing stimulus artifact to the amplitude of the QRS complex was usually smallest." See Adam et al. at 210. Lead I, however, provides only limited information regarding the electrophysiologic processes occurring in the heart.
There have been occasional reports in the human literature noting the presence of T-wave alternans in the precordial leads. However, there has been no suggestion of a superior lead configuration from the body surface which permits measurement of alternans as a quantitative predictor of susceptibility to ventricular fibrillation and sudden death. For example, alternans have been observed in precordial leads V.sub.4 and V.sub.5 during a PCTA (Percutaneous Transluminal Coronary Angioplasty) procedure on a fifty year-old man. M. Joyal et al., "ST-segment alternans during percutaneous transluminal coronary angioplasty," Am. J. Cardiol., vol. 54, pp. 915-916 (1984). Similarly, alternans were noted in precordial leads V.sub.4 through V.sub.6 on a forty-four year-old man during and following a treadmill exercise. N. Belic, et al., "ECG manifestations of myocardial ischemia," Arch. Intern. Med., vol. 140, pp. 1162-1165 (1980).
Dispersion of repolarization has also been integrally linked to cardiac vulnerability and has recently received considerable attention as a potential marker for vulnerability to ventricular fibrillation. The basis for this linkage is that the extent of heterogeneity of recovery of action potentials is directly related to the propensity of the heart to experience multiple re-entrant currents, which initiate and maintain fibrillation and culminate in cardiac arrest. B. Surawicz, "Ventricular fibrillation," J. Am. Coll. Cardiol., vol. 5, pp. 43B-54B (1985); and C. Kuo, et al., "Characteristics and possible mechanism of ventricular arrhythmia dependent on the dispersion of action potential duration," Circulation, vol. 67, pp. 1356-1367 (1983).
The most commonly employed non-invasive approach for measuring dispersion is to obtain body surface maps to define the distribution of T-wave isopotentials and thus estimate the degree of unevenness of repolarization and susceptibility to ventricular fibrillation. F. Abildskov, et al., "The expression of normal ventricular repolarization in the body surface distribution of T potentials," Circulation, vol. 54, pp. 901-906 (1976); J. Abildskov and L. Green, "The recognition of arrhythmia vulnerability by body surface electrocardiographic mapping," Circulation, vol.75 (suppl. III), pp. 79-83 (1987); and M. Gardner, et al., "Vulnerability to ventricular arrhythmia: assessment by mapping of body surface potential," Circulation, vol. 73, pp. 684-692 (1986). Although this approach has been in existence for over 15 years, it has received minimal usage in the clinical setting. The basis for this is that the technique is cumbersome, as it requires over 100 leads on the chest and extensive computerized analysis. Thus, it is used in only a few specialized research centers.
Recently, these has been interest in analyzing QT interval dispersion in the standard 12-lead ECG as a measure of vulnerability to life-threatening arrhythmias. The mathematical transformation required is relatively straightforward as it involves mainly subtraction of a minimum QT interval from a maximum QT interval and determining the variance of the difference. For example, it has been found that QT dispersion is an indicator of risk for arrhythmia in patients with the long QT syndrome, who have greatly enhanced susceptibility to catecholamines released by the nervous system. C. Day, et al., "QT dispersion: an indication of arrhythmia risk in patients with long QT intervals," Br. Heart J., vol. 63, pp. 342-344 (1990). These observation were confirmed and extended in C. Napolitano, et al., "Dispersion of repolarization: a marker of successful therapy in long QT syndrome patients abstract!," Eur. Heart J., vol. 13, p. 345 (1992).
The present inventors' experimental studies have demonstrated that the variance of T-wave dispersion in the epicardial electrogram exhibits a highly significant predictive value in estimating risk for ventricular fibrillation during acute myocardial ischemia. R. Verrier, et al., "Method of assessing dispersion of repolarization during acute myocardial ischemia without cardiac electrical testing abstract!," Circulation, vol. 82, no. III, p.450 (1990). Furthermore, their data has demonstrated that a linear relationship exists between the epicardial and the precordial ECG. See U.S. Pat. No. 5,148,812. This provides the scientific basis for utilizing precordial T-wave dispersion as a measure of the degree of heterogeneity of repolarization which occurs within the heart.
Napolitano et al., supra, have shown in human subjects afflicted with the long QT syndrome that the variance of QT interval in the six standard precordial leads of the ECG is more accurate than the limb leads in estimating risk of life-threatening arrhythmias. These investigators have also demonstrated that dispersion of QT interval also provided a marker of successful therapy in patients receiving beta-blockade therapy and those undergoing cervical ganglionectomy.
Within the last year, it has been demonstrated that QT interval dispersion can predict the development of Torsades de Pointes, a precursor arrhythmia to ventricular fibrillation in patients receiving antiarrhythmic drug therapy. T. Hii, et al., "Precordial QT interval dispersion as a marker of torsades de pointes: disparate effects of class la antiarrhythmic drugs and amiodarone," Circulation, vol. 86, pp. 1376-1382 (1992).
Another method which has been explored to assess autonomic nervous system activity, the neural basis for vulnerability to sudden cardiac death, is analysis of heart rate variability (HRV). Heart rate variability, however, is not an absolute predictor of SCD because there are major, non-neural factors which contribute to sudden death. These include: coronary artery disease, heart failure, myopathies, drugs, caffeine, smoke, environmental factors, and others. Accordingly, techniques which rely on heart rate variability to predict cardiac electrical stability are not reliable.
Further, conventional techniques for analyzing heart rate variability have relied on power spectrum analysis. See, for example, Glenn A. Myers et al., "Power spectral analysis of heart rate variability in sudden cardiac death: comparison to other methods," IEEE Transactions on Biomedical Engineering, vol. BME-33, No. 12, December 1986, pp. 1149-1156. As discussed above, however, power spectrum (Fourier) analysis averages time functions over an entire time series so that rapid arrhythmogenic changes are not detected.
Complex demodulation as a method for analyzing heart rate variability is discussed in Shin et al., "Assessment of autonomic regulation of heart rate variability by the method of complex demodulation," IEEE Transactions on Biomedical Engineering, vol. 36, No. 2, February 1989, which is incorporated herein by reference. Shin et al. teach a method of evaluating the influence of autonomic nervous system activity during behavioral stress. A technique of complex demodulation is used to analyze the pattern of beat-to-beat intervals to determine the relative activity of the sympathetic and parasympathetic nervous systems. While Shin et al. exploited the dynamic analytical characteristics of complex demodulation, they did not relate their results to cardiac vulnerability.
Similarly, T. Kiauta et al. "Complex demodulation of heart rate changes during orthostatic testing," Proceedings Computers in Cardiology, (Cat. No. 90CH3011-4), IEEE Computer Society Press, 1991, pp. 159-162, discusses the use of complex demodulation to assess heart rate variability induced by the standing-up motion in young healthy subjects. Using the technique of complex demodulation, Kiauta et al. conclude that the complex demodulate of the high frequency band probably reflects parasympathetic activity, but the complex demodulate of the low frequency band does not seem to indicate sympathetic activity. Similar to Shin et al., Kiauta et al. do not relate their results to cardiac vulnerability.
In summary, analysis of the morphology of an ECG (i.e., T-wave alternans and QT interval dispersion) has been recognized as a means for assessing cardiac vulnerability. Similarly, analysis of heart rate variability has been proposed as a means for assessing autonomic nervous system activity, the neural basis for cardiac vulnerability. When researching vulnerability to sudden cardiac death, researchers have conventionally relied on power spectrum (Fourier) analysis. However, power spectrum analysis is not capable of tracking many of the rapid arrhythmogenic changes which characterize T-wave alternans and dispersion and heart rate variability. As a result, a non-invasive diagnostic method of predicting vulnerability to sudden cardiac death by analysis of an ECG has not achieved clinical use.
What is needed is a non-invasive, dynamic method for completely assessing vulnerability to ventricular fibrillation under diverse pathologic conditions relevant to the problem of sudden cardiac death. Among the most significant problems are enhanced discharge by the sympathetic nervous system, behavioral stress, acute myocardial ischemia, reperfusion, effects of pharmacologic agents on the autonomic nervous system, and intrinsic cardiac effects of pharmacologic agents. To accommodate these conditions, the method must not assume stationarity of data and must be sensitive to slowly varying amplitude and phase over time. The diagnostic system must be sensitive to the fact that the area of injury to the heart can vary significantly, that extrinsic as well as intrinsic influences affect the electrical stability of the heart, and that the electrophysiologic end point to be detected must be fundamentally linked to cardiac vulnerability.